Introduction to Machine Learning on AWS
This is your quick-start guide for building and deploying with Amazon Machine Learning. By the end of this learning path, you will be able to apply supervised and unsupervised learning, ML algorithms, deep learning, and deep neural networks on AWS. It includes 3 Hands-on Labs where you will work directly in AWS to create a ML regression model, practice training a neural network, and perform a neural-style transfer using MXNet.

Introduction to Azure Machine Learning
For teams without formal AI knowledge, Azure Machine Learning Studio makes machine learning accessible and easy to use. In this learning path, you will be able to create your own machine learning models using the primary machine learning tools on Azure: Azure Machine Learning Studio and Azure Machine Learning Workbench. With Azure Machine Learning Studio, you’ll be able to create your own machine learning models, and you will learn how to use Workbench to deploy a trained model as a predictive web service. The learning path includes courses with built-in demos and a hands-on lab to train and deploy a model in Azure Machine Learning Studio.

Azure Services for Security Engineers
Strong cloud security starts with a full understanding of available services and the skills to properly use them in your environment. Your team will be able to take advantage of Microsoft Azure’s security features for data centers, management applications, and its pay-as-you-go security services. Teams will get hands-on practice using Azure Security Center, Key Vault and Disk Encryption, and RBAC features.

Getting Started with Migrating to AWS
For teams undertaking their first migration of services or applications to AWS, you will learn the core migration strategies, project stages, and services that have been tried and tested in cloud migration projects. By the end of this learning path, your team will be able to select and

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